# HybridoNet-Adapt: A Domain-Adapted Framework for Accurate Lithium-Ion Battery RUL Prediction

arXiv: https://arxiv.org/abs/2503.21392 · HTML: https://mlsanity.com/paper/2503.21392/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 71 references that are in the map.
- First posted on arXiv 27 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Understanding Batch Normalization](https://mlsanity.com/paper/1806.02375/) (2018)
- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [Mean Absolute Percentage Error for regression models](https://mlsanity.com/paper/1605.02541/) (2016)
- [Dynamic ReLU](https://mlsanity.com/paper/2003.10027/) (2020)
- [An Equivalence of Fully Connected Layer and Convolutional Layer](https://mlsanity.com/paper/1712.01252/) (2017)
- [A ReLU Dense Layer to Improve the Performance of Neural Networks](https://mlsanity.com/paper/2010.13572/) (2020)

## Abstract (arXiv)

Accurate prediction of the Remaining Useful Life (RUL) in Lithium ion battery (LIB) health management systems is essential for ensuring operational reliability and safety. However, many existing methods assume that training and testing data follow the same distribution, limiting their ability to generalize to unseen target domains. To address this, we propose a novel RUL prediction framework that incorporates a domain adaptation (DA) technique. Our framework integrates a signal preprocessing pipeline including noise reduction, feature extraction, and normalization with a robust deep learning model called HybridoNet Adapt. The model features a combination of LSTM, Multihead Attention, and Neural ODE layers for feature extraction, followed by two predictor modules with trainable trade-off parameters. To improve generalization, we adopt a DA strategy inspired by Domain Adversarial Neural Networks (DANN), replacing adversarial loss with Maximum Mean Discrepancy (MMD) to learn domain-invariant features. Experimental results show that HybridoNet Adapt significantly outperforms traditional models such as XGBoost and Elastic Net, as well as deep learning baselines like Dual input DNN, demonstrating its potential for scalable and reliable battery health management (BHM).